Ontology highlight
ABSTRACT:
PROVIDER: PRJNA647798 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:The identification of COVID-19 patients with high-risk of severe disease is a challenge in routine care. We performed blood RNA-seq gene expression analyses in severe hospitalized patients compared to healthy donors. Supervised and unsupervised analyses revealed a high abundance of CD177, a specific neutrophil activation marker, contributing to the clustering of severe patients. Gene abundance correlated with high serum levels of CD177 in severe patients. These results highlight neutrophil activation as a hallmark of severe disease and CD177 assessment as a reliable prognostic marker for routine care.
2021-05-27 | GSE171110 | GEO
Project description:Circulating miRNAs in patients who underwent ARDS and needed mechanical ventilation were analyzed by next generation sequencing (NGS) in comparison with patients who had COVID-19 poor symptoms but without intensive care unit requirement.
2022-04-12 | GSE182183 | GEO
Project description:The goal of the study is to compare NGS-derived transcriptome (miRNA-seq) profiles of melanocytes and metastatic melanoma cells and identify microRNAs involved in melanomagenesis
2019-01-16 | GSE109244 | GEO
Project description:Tissue composition is a major determinant of phenotypic variation and a key factor influencing disease outcomes. Although scRNA-Seq has emerged as a powerful technique for characterizing cellular heterogeneity, it is currently impractical for large sample cohorts and cannot be applied to fixed specimens collected as part of routine clinical care. To overcome these challenges, we extended Cell type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) into a new platform for in silico cytometry. Our approach enables the simultaneous inference of cell type abundance and cell type-specific gene expression profiles (GEPs) from bulk tissue transcriptomes. The utility of this integrated framework, called CIBERSORTx, is demonstrated in multiple tumor types, including melanoma, where single cell reference profiles are used to dissect primary clinical specimens, revealing cell type-specific signatures of driver mutations and immunotherapy response. We anticipate that digital cytometry will augment single cell profiling efforts, enabling cost-effective, high throughput tissue characterization without the need for antibodies, disaggregation, or viable cells.
2019-05-06 | GSE127462 | GEO
Project description:Tissue composition is a major determinant of phenotypic variation and a key factor influencing disease outcomes. Although scRNA-Seq has emerged as a powerful technique for characterizing cellular heterogeneity, it is currently impractical for large sample cohorts and cannot be applied to fixed specimens collected as part of routine clinical care. To overcome these challenges, we extended Cell type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) into a new platform for in silico cytometry. Our approach enables the simultaneous inference of cell type abundance and cell type-specific gene expression profiles (GEPs) from bulk tissue transcriptomes. The utility of this integrated framework, called CIBERSORTx, is demonstrated in multiple tumor types, including melanoma, where single cell reference profiles are used to dissect primary clinical specimens, revealing cell type-specific signatures of driver mutations and immunotherapy response. We anticipate that digital cytometry will augment single cell profiling efforts, enabling cost-effective, high throughput tissue characterization without the need for antibodies, disaggregation, or viable cells.
2019-05-06 | GSE127813 | GEO
Project description:Tissue composition is a major determinant of phenotypic variation and a key factor influencing disease outcomes. Although scRNA-Seq has emerged as a powerful technique for characterizing cellular heterogeneity, it is currently impractical for large sample cohorts and cannot be applied to fixed specimens collected as part of routine clinical care. To overcome these challenges, we extended Cell type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) into a new platform for in silico cytometry. Our approach enables the simultaneous inference of cell type abundance and cell type-specific gene expression profiles (GEPs) from bulk tissue transcriptomes. The utility of this integrated framework, called CIBERSORTx, is demonstrated in multiple tumor types, including melanoma, where single cell reference profiles are used to dissect primary clinical specimens, revealing cell type-specific signatures of driver mutations and immunotherapy response. We anticipate that digital cytometry will augment single cell profiling efforts, enabling cost-effective, high throughput tissue characterization without the need for antibodies, disaggregation, or viable cells.
2019-05-06 | GSE127471 | GEO
Project description:next-generation sequencing (NGS) of Chinese patients with melanoma
2022-01-20 | PRJEB50175 | EVA
Project description:Genetic abnormalities including copy number variants (CNVs, such as gains and losses), and gene mutations are important for diagnosis and treatment of myeloid malignances. In a routine clinical setting, somatic gene mutations are detected by targeted next generation sequencing (NGS), but CNVs are commonly detected by conventional chromosome analysis and fluorescence in situ hybridization (FISH). The aim of this proof-of-principle study was to investigate the feasibility of using a targeted NGS assay to simultaneously detect not only somatic mutations, but also CNVs. Here, we sequenced 406 consecutive patients with myeloid malignancies and performed a head-to-head comparison with the results from conventional clinical assays including conventional chromosome analysis and myeloid FISH to detect CNVs. The targeted NGS assay revealed all 120 CNVs detected by myeloid FISH panel including monosomy 5/5q deletions, monosomy 7/7q deletions, trisomy 8, and 20q deletions. Furthermore, the targeted NGS assay also detected 605 CNVs outsides targeted regions of the myeloid FISH panel, which were revealed by conventional cytogenetic testing. The targeted NGS assay achieved 100% concordance with the myeloid FISH for detection of these common myeloid CNVs, with a high clinical sensitivity (> 99%) and specificity (>99%). The lower limit of detection by the myeloid FISH and the targeted NGS assay was similar and was generally 5% variant allele fraction for DNA. This proof-of-principle study demonstrated that the targeted NGS assay can simultaneously detect both common myeloid CNVs and somatic mutations, which can provide more comprehensive genetic profiling for patients with myeloid malignancies using a single assay.
2021-01-29 | GSE165733 | GEO
Project description:NGS-based pipeline for routine non-invasive pre-implantation genetic assessment in IVF
| PRJEB38821 | ENA
Project description:The goal of the study is to compare NGS-derived transcriptome (mRNA-seq) profiles of melanocytes and metastatic melanoma cells and identify genes involved in melanomagenesis
2019-01-16 | GSE109245 | GEO